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Copyright of Die Radiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Herzschrittmachertherapie und Elektrophysiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Beton- Und Stahlbetonbau is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Beton- Und Stahlbetonbau is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Urologe A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Politische Vierteljahresschrift is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although the accounting definition of assets contemplates intangible, abstract assets such as those embodied in intellectual property (IP), South African company law largely views IP as a legal and not a business asset. This paper tentatively suggests an approach that uses artificial intelligence (AI) to mitigate weaknesses in the South African patent law relating to the absence of patent searches and examinations. It is hoped that using AI will enable the filing of quality patents that satisfy the prescribed patentability criteria. High-quality patents will allow companies to accumulate patents as corporate assets. The approach is based on the algorithmic use of AI technologies such as machine learning, natural language processing, deep learning alongside the Internet of Things, and IP analytics to strengthen South Africa’s IP system and create asset value for corporations. The paper recommends using the proposed AI technologies by companies and the Patents Office to enable the filing of high-quality patents, which will lead to the accumulation of corporate assets in the form of patents. The methodology is doctrinal, and the paper relies on recent literature on IP and AI, South African law, case law and examples drawn from studies conducted in other countries.

The Center for Data Analysis, Visualization, and Simulation (DAViS) at the University of Applied Science of the Grisons and the Swiss Center of Allergy and Asthma Research supports research on applications in topics like machine learning, big data, visualization, and simulation. The paper illustrates complex data analysis of life science data in a common research project. [ABSTRACT FROM AUTHOR]

Algorithms have become an indispensable part of everyday business life. They not only screen markets within a very short time, but also influence the market behavior of companies. In the age of ChatGPT and “deep learning algorithms”, many intriguing questions arise: Can algorithms be “cartel promoters”, “cartel facilitators” or even “cartel initiators”? Is the antitrust concept of “concurrence of wills” still up-to-date? When can the “conduct” of algorithms be attributed to a company? This practice-oriented article is intended to shed light on problematic constellations on the basis of a number of cases. [ABSTRACT FROM AUTHOR]

Copyright of Die Nephrologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rise of digital humanities, historians are exploring how to intellectually engage with textual sources given the computational tools available today. The ENP-China project employs Natural Language Processing methods to tap into sources on an unprecedented scale, with the goal of studying the transformation of elites in Modern China (1830 – 1949).1 One of the subprojects is extracting various kinds of data from biographies, for which we created a large corpus of biographies automatically collected from the Chinese and English versions of Wikipedia. The dataset contains 228,144 biographical articles from the offline Chinese Wikipedia copy and is supplemented with 110,713 English biographies that are linked to a Chinese page. We also enriched this bilingual corpus with metadata that records every mentioned person, organization, geopolitical entity and location per Wikipedia biography and links the names to their counterpart in the other language. This data structure allows the researcher to analyze the relationships between biographies via shared contents and compare networks in different language settings. In this paper, we will describe our methodology for building this new dataset. The first step was to use automatic text classification for extracting Chinese biographies. We trained a binary classifier to detect biographies on manually classified examples and used a subset of unseen texts to assess its accuracy. The second step used Named Entity Recognition to generate metadata and extract relations from the links in Wikipedia. Furthermore, we will delve into the method for building networks from this dataset. We argue that depending on the specific research question, different networks may be built. Using the metadata, researchers can create various kinds of networks to suit their needs. As well as releasing this dataset as an enriched bilingual corpus, we will provide an online interface to query and explore it. Our interface benefits from a bipartite graph structure (which can be seen as a network of documents and entities) and applies the same exploration and clustering strategy found in Cillex.2

Copyright of Der Urologe A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Apart from traditional deep learning tasks such as pattern recognition, stock price prediction, and machine translation, this method also finds practical application within algorithmic composition. This paper explores the use of a generative model based on unsupervised learning of a musical style from a pre-selected corpus and the subsequent prediction of samples from the estimated distribution. The model uses a Long Short-Term Memory neural network whose training data contains genre-specific melodies in symbolic representation.

Copyright of Journal of Cultivated Plants / Journal für Kulturpflanzen is the property of Verlag Eugen Ulmer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Urologe A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Praxis (16618157) is the property of Aerzteverlag medinfo AG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Public Law / Zeitschrift für Öffentliches Recht is the property of Verlag Oesterreich GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)